Adaptive electric shocks control and elimination of spiral waves using dynamic learning based techniques

The elimination of cardiac spiral waves by high-voltage defibrillation has significant side effects, and low-intensity alternatives have long been in demand. The advances in AI optimization techniques offer promising solutions for adaptive control and elimination of spiral waves. This study is the first attempt to apply the dynamic learning of synchronization (DLS) technique to excitable media, where the electrodes are trained with nodal membrane potentials to deliver adaptive electric shocks (AES) for energy-efficient defibrillation. We discuss the global AES, local AES for tracking the tip of spiral waves, and the effects of electrode resolution on defibrillation. Our findings indicate that AES with moderate intensity and applied area rapidly induces excitable media synchronization with high energy efficiency. The resolution of the electrode array to accomplish electrical defibrillation depends on the characteristics of the spiral waves (model-related), specifically, the electrode size must be smaller than the wave wall. Most importantly, this study demonstrates that the total energy released by AES is 1–2 orders of magnitude lower than high-voltage shocks in eliminating spiral waves. This finding may offer valuable insights into the potential application of AI optimization techniques for low-energy defibrillation.